An innovative method and application for on-line

An innovative method and application for on-line detection of battery SOC and SOH Guilin Zheng, Zhihao Tao Dept. of Automation, Wuhan University,Zip:430072 Abstract: Management and instant detection and evaluation of a battery SOC and SOH are bottle neck in all the relative field, such as: electric vehicle, communication, electricity and other applications. Accurate State of Charge (SOC) and State of Health (SOH) measurement and detection of storage batteries play a significant role in the monitoring of the state of the battery to know the energy content online, avoiding dangerous operating conditions and extending battery life. In this paper we evaluate the SOH between the relationship of the battery internal resistance and its available capacity (SOC) as well as creating an innovative online SOC and SOH method. The accurate detection of the battery internal resistance and synchronous detection of battery’s maximum current and voltage in transient release forms the basis of the methodology. The result is a portable detection system designed for online SOC and SOH detection, which performed on application showed that the accuracy of SOC and SOH detection can reach 95.5% and 96.4% respectively. Thus, it is a very effective and innovative instrument for fast quality identification, maintenance and management of storage battery. Keywords: storage battery; SOC; SOH; internal resistance; on-line detection;

1. Introduction With advance production technology, relative low cost, large discharge current, large capacity, high reliability, and other performance advantages, storage batteries were widely used in many fields, such as electric vehicles, communication, electricity and so on [1]. It has been widely recognized that accurate detection of State of Charge (SOC) and State of Health (SOH) play significant roles in reliable and safe usage of storage batteries. The State of Charge (SOC) is an important parameter reflecting the performance of storage battery. When the SOC dropped below 90% of the rated capacity, the battery would enter a degenerating stage. The State of Health (SOH) can be used to recognize an ongoing or a sudden degradation of the battery cells and to prevent a possible failure of the electric system. Thus, one of the most important tasks within the battery management system (BMS) is a reliable real time determination of the SOC and SOH. At present, the widely accepted method in SOC and SOH detection is the process of counting the discharged energy of the battery during a controlled constant load or constant current, also known as the load discharge method [2]. It is obvious that the time and labor required to apply this method is not very practical. This approach requires interrupting the normal operation of the battery system, and causes significant amount of wasted energy during tests and affects battery service life. Other traditional forecasting methods of battery’s state mainly include: 1) Electric liquid density method

[3]

, by detecting the density of battery electrolyte to predict

the state of the battery. The limitations of this approach includes not being suitable for Valve

Regulated Lead Acid Battery (VRLA) and SOH cannot be predicted where the prediction error of SOC becomes larger with the aging or deterioration of battery. 2) Open circuit voltage (OCV) method

[4]

, by measuring the open circuit voltage of the

battery to predict the state of battery. The disadvantage is that the battery needs a long standing time to wait for voltage stabilization. When the battery ages and deteriorates after using for a long time, the battery’s capacity drops, and the change of open circuit voltage cannot reflect the real state of capacity. 3) The ampere or coulomb counting method

[5]

, which is based on the capacity-scaled

integration of the battery current, is the most widely used on-line direct strategy. However, it is prone to several runtime errors related to complete dependence of the method on the accuracy of current measurement and increase difficulty of online assessment of the actual battery capacity. Also, without an accurate initial SOC, the method does not allow for detection correction at a later stage. 4) Internal resistance method [6], the battery’s resistance-capacity curve must be measured in this method. The measuring process is complicated, hence the lack of popularity. However, the internal resistance characterizes the useful energy and attainable power to some extent respectively. In order to improve the reliability and precision of SOC and SOH detection of the storage battery, the accurate detection of battery’s internal resistance and synchronous detection of battery’s maximum current and voltage in transient release is required in which a new SOC and SOH detection method was proposed in this paper. The core algorithm is a self-correction detection method of SOC and SOH based on Gauss-Newton iteration algorithm. Experiment and application results are shown to be a very effective new method of on-line battery monitoring, quality inspection and fault warning, which can be used as a standing means of battery management and maintenance, to extend battery life, improve energy efficiency and reduce environmental pollution.

2. Principle of battery state detection 2.1 Definitions of SOC and SOH State of Charge (SOC) refers to the remaining power of storage batteries, with regards to the battery charge storage capacity. Generally speaking, SOC is the ratio of remaining power to the rated capacity under the same condition [7]. SOC is defined as follows: C SOC = remain Crated In Eq. (1), Cremain is battery remaining capacity. Crated is battery rated capacity.

(1)

State of Health (SOH) is represented by the deterioration of the battery, the degree of agingand the remaining life of the battery. With battery usage and deteriation, the value of SOH will continue to decrease. Therefore SOH is essentially a ratio between the maximum energy that can be depleted out of battery or the maximum energy that can be stored in battery’s total capacity

[8]

a completely new

. SOH is then defined as follows: C (2) SOH = max Cnew In Eq. (2), Cmax is the maximum capacity that can be depleted out of battery or be stored in it.

Cnew is a new battery’s total capacity.

2.2 Modelling of SOC and SOH detection Even though the importance of SOC and SOH is high, there is still no consensus within the industry or in the scientific community on what SOC and SOH are and how it should be estimated. As the most important indicator of battery, the internal resistance is crucial for estimating the State of Charge and the State of Health. Many studies show that there exists a close relationship between internal resistance, voltage, SOC and SOH of battery [9-10], which is shown in fig.1. SOC

Voltage

SOH SOC

Internal Resistance

Internal Resistance

(a) internal resistance, voltage vs SOC

(b) internal resistance, SOC vs SOH

Fig.1 The relationship between internal resistance, voltage, SOC and SOH

Fig.1(a) shows that with the increase of battery voltage, SOC increases approximately with a Gaussian curve; while a increase in internal resistance, SOC gradually decreases creating an inversely proportional relationship. Fig.1(b) shows that with an increase of internal resistance, SOH decreases quadratically; with the increase of SOC, SOH gradually increases linearly. According to the above relationships of the characteristics of the battery state, a simple model for estimating SOC and SOH is proposed based on battery voltage, internal resistance and maximum current. With a self-correction Gauss-Newton algorithm, this model can achieve accurate, convenient and intelligent detection of the SOC and SOH. Irrespective of temperature, chemical changes and other factors, the SOC and SOH detection model can be expressed as follows:

SOC =

2 ⎛ (V −k ) ⎞ −⎜ Load 2 ⎟ 2 ⎜ ⎟ k3r ⎠ k1e ⎝

+ k4 I max + k5

SOH = p1r 2 + p2r + p3SOC + p4rSOC + p5

(3) (4)

In Eq. (3) and Eq. (4), SOC ∈ [0,100] , SOH ∈ [0,100] , VLoad is battery voltage, r is the internal resistance of the battery, I max is the maximum current, k1 , k2 , k3 , k4 , k5 and p1 , p2 , p3 , p4 ,

p5 are undetermined coefficients.

2.3 Principle and method of internal resistance detection The internal resistance is an important parameter that indicates battery discharge capacity and determines the current state of the battery, which is also the most important indicator of the remaining battery capacity (SOC) and state of health (SOH) [11]. It also reflects the difficulty of the ion and electron transport between the positive and negative electrodes during a chemical reaction inside the battery. There are two methods of internal impedance measurement of a battery: DC

current discharge method and AC signal injection method

[12]

. With the influence of capacitance

effect, AC signal injection method measuring the internal resistance has inaccuracies, especially with a high test signal frequency; it results in less accurate measure of internal resistance. In contrast, the DC current discharge method can effectively avoid the influence of the capacitance effect. Therefore, a four-wire DC current discharge method is presented to detect internal resistance, and the detection principle is shown in Fig.2. Fig.2(a) shows the Thevenin equivalent model of battery, and Fig.2(b) shows the transient discharge process of the battery. In Fig.2, E0 is an ideal voltage source, RΩ is ohmic resistance, Rc is polarization resistance, C is electrode capacitance, I is discharge current, Vo is battery voltage, ΔV is voltage drop.

RΩ

C

Rc

+

+

E0 -

∆V

Vo

Vo

I

I

(a) Thevenin equivalent model

(b) The transient discharge process

Fig.2 The principle of internal resistance detection

When measuring with DC current discharge method, the internal resistance is equivalent to the ohmic resistance and polarization resistance. The internal resistance is described and shown in Eq. (5) and Eq. (6) following the Ohm’s law. E0 = I ( RΩ +Rc ) + Vo = Ir + Vo

(5)

The internal resistance calculation formula assumes the following form: r=

E0 − Vo ΔV = I I

(6)

In order to accurately measure internal resistance on-line, an internal resistance measurement subsystem is designed with a microcontroller core of MC9S12XS128, shown in Fig.3. Four-wire measurement method is applied, which separates voltage sampling and current sampling resulting in elimination of interference of line resistance and contact resistance to measurement. Voltage sampling circuit is constituted with a precision resistor divider network and an operational amplifier AMP. Current sampling circuit is constituted with the hall element and differential amplifier circuit with an operational amplifier AMP. Microcontroller MC9S12XS128 control the discharge switch SW by isolating driver OP, making the battery produce higher current pulse, as shown in Fig.2 (b). With the high-speed analog-digital conversion module of MC9S12XS128, voltage and current data are synchronous and sampling calculates the internal resistance of the battery according to Eq. (6). Measuring internal resistance with this high current pulse discharge method is not only beneficial to accurately measure the internal resistance of the battery, but also activates the battery, avoiding the electrode of long-term non-discharge battery being surrounded and besieged by hydrogen bubbles, resulting in "passive" issue of battery.

OP LOAD

SW

Clip + AMP −

Battery

Voltage Sampling

Clip

Hall Element I

ADC

+ AMP −

MC9S12XS128

Wires

Current Sampling

Fig.3 The internal resistance measurement subsystem

3. Design of battery state detection system 3.1 Hardware architecture In order to measure internal resistance, voltage, current and other parameters, and finally realize the detection of SOC and SOH, the hardware scheme of portable battery SOC and SOH detection system is designed, and shown in Fig.4. With high-speed, low-power and high anti-interference microcontroller of MC9S12XS128, the system mainly consists of overcurrent protection circuit, voltage and current sampling circuit, isolated driver circuit, internal resistance measurement subsystem, constant-current discharge subsystem, battery charging subsystem, LCD display, communication interfaces and other components. Overcurrent Protection

Battery

Switch

Internal Resistance Measurement Subsystem Sampling Load

Voltage

Current

Isolated Driver

Radiator

MC9S12XS128

Touch Screen

LCD Display

Communication Interface (RS485/WiFi/Ethernet)

Constant-current Discharge Subsystem Discharge Load

Battery Charging Subsystem

Charger

Fig.4 The diagram of system hardware scheme

There are two main functions are described as follows: 1) Discharge detection, microcontroller controls the internal resistance measurement subsystem by isolated driver to release a large transient current, and the constant-current discharge

subsystem so that the battery is in a state with a load discharge rate of 0.1C. With synchronous measurement of internal resistance, discharge current, voltage and other data, the quantitative results of SOC and SOH is estimated by Eq. (3) and Eq. (4). 2) Self-correction, microcontroller control battery charging subsystem to make the battery reach a fully charged state, and then discharge the battery with a discharge rate of 0.1C until the battery is fully discharged. According to the associated data of measured internal resistance, voltage, discharge current, SOC and SOH, the detection model is corrected by the Gauss-Newton algorithm, see Section 3. Internal resistance, voltage, discharge current, discharge time, and estimated SOC, SOH, expected service life and other data are displayed real-time on the LCD. With the communication interfaces of RS485, Wi-Fi, Ethernet, etc., it can be realized with a computer or mobile phone and other terminal to interact inreal-time with on-line monitoring, which improves system flexibility, efficiency and extends the range of its practical application. 3.2 Software architecture The main program of the battery state detection system is an unending cycle, according to the user’s instructions to test and estimate the battery state, the software architecture is shown in Fig.5. (1) Initialize hardware modules, such as system clock, IO port, ADC converter, timers, serial communication, LCD display, etc. (2) According to the instruction, it determines whether detection of the battery state is needed. If yes, system controls internal resistance measurement subsystem to discharge and detect, and then calculate SOC and SOH. (3) According to the instruction correct detection model is determined. If correction is needed, system controls the battery charging subsystem and the constant-current discharge subsystem to fully charge and discharge the battery. It then calculates and corrects the model parameters with Gauss-Newton iteration algorithm. (4) After sending the results of estimated battery state or modified model parameters to the LCD display and communication terminals, the program re-enters the main cycle. Start

Initialization

Estimate？

N

Y

Self-Correct？ Y

Internal Resistance Measurement Subsystem

Battery Charging Subsystem

Calculate SOC and SOH

Constant-Current Discharge Subsystem

LCD Display & Communication Terminals

Correct Estimation Model

N

Fig.5 The flow chart of main program

3.3 Human machine interaction Designed by the proposed detection method with hardware and software implementation, the appearance of portable battery SOC and SOH detection system is shown in Fig.6. To facilitate the interaction with user, C ++ language design is used to test the product’s human machine interaction, as shown in Fig.7. HMI not only can display the battery voltage, current, internal resistance, and other parameters, but also can display the estimated remaining battery capacity (SOC), state of health (SOH), expected service life and other information. In addition, users can also monitor battery state, control charge or discharge of the battery by PC software and mobile phone APP. Battery

Clip Measure Wire

LCD

Power Switch

Discharge Wire

SOC & SOH Estimation System

Fig.6 The product appearance

Fig.7 The human machine interaction

4. Gauss-Newton algorithm of self-correction By Eq. (3) and Eq. (4), estimating SOC and SOH requires the data of voltage, internal resistance and maximum current. Based on battery type, contact resistance (affect maximum current), circuit board technology and other factors, the measurement results under the same circumstances will vary, resulting in the battery state detection error becomes larger. Therefore, the battery state detection model should have the properties of self-learning, self-correction, self adapting, in order to improve the stability and accuracy of the detection results. In this paper, Gauss-Newton algorithm of self-correction, with Taylor series expansion equation is used to approximate the detection model. Through multiple iterations, model parameters converges so that the model parameters are closer and closer to the optimal parameters, and the residual sum of squares is minimized. The calculation process is as follows: (1) assume that the detection model is: (7) yi = f ( xi , r ) + ε i Where, i = 1, 2,L , n , r is undetermined coefficient, ε i is the error term, assuming that g(0) = ( g 0(0) , g1(0) ,L , g (0) p −1 ) is the initial value of undetermined coefficient r , ignoring the second and higher order partial derivative term, the Taylor expansion of f ( xi , r ) near g (0) is as follows:

f ( xi , r ) ≈ f ( xi , g (0) ) + Put Eq. (8) into Eq. (7), and calculate:

p −1

⎡ ∂f ( xi , r ) ⎤ (rk − g k(0) ) ⎥ ∂ r k ⎦ r = g (0) k =0

∑ ⎢⎣

(8)

yi ≈ f ( xi , g (0) )+

p −1

⎡ ∂f ( xi , r ) ⎤ (rk − g k(0) ) + ε i ⎥ ∂ r (0) k ⎦r =g k =0

∑ ⎢⎣

(9)

Writing Eq. (9) in the following format: yi(0) ≈

p −1

∑ Dik(0) βk(0) + ε i

(10)

k =0

⎡ ∂f ( xi , r ) ⎤ Where, yi(0) = yi − f ( xi , g (0) ) , Dik(0) = ⎢ , β k(0) = rk − g k(0) . ⎥ ⎣ ∂rk ⎦ r = g (0)

Eq. (10) is expressed in matrix type:

Y (0) ≈ D(0) B(0) + ε Where, Yn(0) ×1

(0) ⎛ D10 ⎛ y1 − f ( x1 , g (0) ) ⎞ L ⎜ ⎜ ⎟ (0) =⎜ M ⎟ , Dn× p = ⎜ M O ⎜ (0) ⎜ (0) ⎟ ⎜ Dn 0 L ⎝ yn − f ( xn , g ) ⎠ ⎝

(11)

⎞ ⎛ β (0) ⎞ D1(0) p −1 ⎟ ⎜ 0 ⎟ M ⎟ , B (0) = ⎜ M ⎟. p×1 ⎜ (0) ⎟ (0) ⎟ ⎜ β p −1 ⎟ Dnp −1 ⎟⎠ ⎝ ⎠

(2) The correction of factor B (0) by using the least squares method is given by: T

T

B(0) = ( D(0) D(0) )−1 D(0) Y (0)

(12)

Assume g (1) = g (0) + B(0) is the first iteration. (3) Test the accuracy of model correction. The residual sum of squares after s times iteration is described by: SSR(0) =

n

∑ ⎡⎣ yi − f ( xi , g (s) )⎤⎦

2

(13)

i =1

If the allowable error rate is k, when

SSR( s ) − SSR( s −1)

≤ k , then iteration process is stopped; SSR( s ) Otherwise, continue to iterate until the error rate is less than the allowable value k. Calculated by the algorithm above, one of the detection models is as follows:

SOC =

2 ⎛ (V −10.412) ⎞⎟ −⎜ Load 2 ⎜ ⎟ 0.30265r ⎠ −167.411e ⎝

+ 0.00618I max + 166.273

(14)

(15) SOH = 102.6 + 1.074r + 1.454SOC − 0.2476r 2 − 0.3195rSOC In Eq. (14) and Eq. (15), SOC ∈ [0,100] , SOH ∈ [0,100] , VLoad is battery voltage, r is the internal resistance, I max is the maximum current.

5. Experimental results 5.1 Detection of SOC In this paper, batteries named A, B, C are used as the test subjects, which rated capacities are 100AH, 150AH and 300AH respectively. During the battery discharge process, using self-developed battery state detection system to estimate the remaining capacity (SOC), the result is shown in Fig.6. First, according to the Eq. (14) to automatically estimate the remaining capacity of the battery Psoc , and then use the battery charge and discharge monitor ART-5780 as a test tool

to measure the actual remaining capacity Asoc . The results are shown in Tab.1, where No.1 ~ 9 is the battery A, No.10 ~ 17 is the battery B, No.18 ~ 26 is the battery C.

Tab.1 The experimental data of SOC detection ASOC

PSOC

eSOC

（%）

（%）

（%）

ASOC

PSOC

eSOC

（%）

（%）

（%）

1

91

92.2

1.3

14

14

12.9

7.9

2

81

78.9

2.6

15

9

8.7

3.4

3

71

69.6

1.9

16

4

4.4

9.1

4

61

57.7

5.3

17

2

2.1

6.4

5

51

48.2

5.4

18

93.3

91.8

1.6

6

41

39.4

3.9

19

83.3

83.4

0.2

7

31

31.5

1.7

20

78.3

80.4

2.7

8

21

22.0

4.9

21

68.3

69.6

1.9

9

6

5.6

6.3

22

63.3

67.1

6.1

10

34

37.2

9.5

23

33.3

36.0

8.1

11

29

31.5

8.7

24

28.3

28.6

1.0

12

24

23.5

2.3

25

13.3

13.8

3.7

13

19

17.2

9.7

26

8.3

8.4

1.3

No

No

100

ASOC

SOC (%)

80

PSOC

60

Battery B

40 tex tex tex

20 0 0

Battery A 5

tex

Battery C 10

15

No

20

25

30

Fig.8 The comparison of Psoc and Asoc

Relative Error (%)

15

10

5

0

-5 0

5

10

No

15

20

25

Fig.9 The error of SOC detection

Fig.8 is the comparison graph of Psoc and Asoc of battery A, B, C. Fig.9 is the graph of relative error of SOC detection. It is observed that, Psoc and Asoc shows a good correlation, with a correlation coefficient of 0.9961 and the average relative error of 4.5%, indicating that the system of SOC detection result is consistent with the actual value, and have the same estimate effect of different rated capacity battery. Therefore, the detection method for SOC detection is applicable to a variety of different types of batteries. The main sources of detection error are

measurement errors of voltage, internal resistance, current, battery temperature variation, artificial error and so on. 5.2 Detection of SOH Estimating the quantitative result of SOH is beneficial to the use, maintenance and management of storage battery, in which ensures the battery power system's vitality. In this paper, the actual state of health ASOH is known by fully charge and discharge test of battery A, B, C, while the estimated state of health ESOH is calculated by a self-developed battery state detection system Eq. (15). The results are shown in Tab.2, where No.1 ~ 9 is the battery A, No.10 ~ 17 is the battery B, No.18 ~ 26 is the battery C. Tab.2 The experimental data of SOH detection ASOH

ESOH

eSOH

（%）

（%）

（%）

ASOH

ESOH

eSOH

（%）

（%）

（%）

1

91

93.3

2.5

14

34

32.4

4.6

2

91

93.4

2.8

15

34

36.2

6.5

3

91

94.9

4.3

16

34

33.0

3.0

4

91

95.7

5.2

17

34

32.5

4.5

5

91

95.4

4.8

18

95

94.0

1.0

6

91

94.9

4.3

19

95

89.9

5.3

7

91

93.6

2.9

20

95

90.0

5.2

8

91

87.5

3.8

21

95

94.1

0.9

No

No

9

91

92.9

2.2

22

95

92.6

2.6

10

34

35.3

3.8

23

95

88.7

6.6

11

34

34.9

2.8

24

95

90.2

5.0

12

34

33.0

3.0

25

95

92.4

2.7

13

34

35.0

3.1

26

95

94.2

0.8

100 90

SOH (%)

80 70

Battery A

Battery C Battery B

60 50

ASOH

40

ESOH

30 0

5

10

15

No

20

25

Fig.10 The comparison of ESOH and ASOH

Relative Error (%)

10

5

0

-5 0

5

10

15

No

20

Fig.11 The error of SOH evaluation

25

Fig.10 is the comparison graph of ESOH and ASOH of battery A, B, C. Fig.11 is the graph of relative error of SOH detection. It is observed that, ESOH and ASOH shows a good correlation, with a correlation coefficient of 0.9899 and the average relative error of 3.6%, indicating that the system of SOH detection result is consistent with the actual value, and have the same estimated effect of different rated capacity battery. Therefore, the SOH detection method is applicable to a variety of different types of battery. The main sources of detection error are measurement error, SOC detection error, parameters changes after battery’s usage and so on.

6. Conclusion Aiming at the problem of battery SOC and SOH detection, this paper introduces a self-correction detection method using Gauss-Newton algorithm on the basis of accurate detection of battery’s internal resistance and synchronous detection of battery’s maximum current and voltage in transient release. Based on this method, a self-developed portable detection system can measure real-time and on-line internal resistance, voltage, current and other characteristic parameters, and also efficiently estimate SOC and SOH of batteries accurately and provide information to the user about the necessity of replacing an old or damaged battery bank. The result of the experiment showed that the accuracy of SOC and SOH detection can reach 95.5% and 96.4% respectively. The self-developed portable detection system can be widely used in battery power management system for field testing and on-line monitoring, as well as to identify and warn of old or damaged battery bank, with potentially broad application prospective.

References [1] MARCHILDON J, DOUMBIA M L, AGBOSSOU K. SOC and SOH Characterisation of Lead Acid Batteries[C], Industrial Electronics Society, IECON 2015-41st Annual Conference of the IEEE, 2015.. [2] R S Robinson, R Bank, On-line battery testing: a reliable method for determining battery health, in: Telecommun. Energy Conf. 1996. INTELEC'96., 18th, 1996, pp. 654e661. [3] Yang X, Jiang F, Wu X. Prediction of Lead-acid Storage Battery's Remaining Capacity Based on LM-BP Neural Network[C], Control and Decision Conference. 2013:1908 - 1912. [4] M. Ceraolo, “New dynamical models of lead-acid batteries,” Power Systems, IEEE Transactions on, vol. 15, pp. 1184 –1190, nov 2000. [5] K. Kutluay, Y. Cadirci, Y. Ozkazanc, and I. Cadirci, “A new online state-of-charge detection and monitoring system for sealed lead-acid batteries in telecommunication power supplies,” Industrial Electronics, IEEE Transactions on, vol. 52, pp. 1315 – 1327, oct. 2005. [6] Wang Sicheng, Chen Ziping, Yang Jun, Yu Anye. SOC Moeling For Lead-acid Battery And Developments Of SOC On-line Tester[J]. ACTA ENERGIAE SOLARIS SINICA, 2005, 26(1):6-13. [7] Dragicevic T, Sucic S, Guerrero J M. Battery state-of-charge and parameter detection algorithm based on Kalman filter[C]. EUROCON, 2013 IEEE. 2013:1519-1525. [8] Tong S, Klein M P, Park J W. On-line optimization of battery open circuit voltage for improved state-of-charge and state-of-health detection[J]. Journal of Power Sources, 2015, 293:416-428. [9] Burgosmellado C, Orchard M E, Kazerani M, et al. Particle-filtering-based detection of maximum available power state in Lithium-Ion batteries[J]. Applied Energy, 2016, 161. [10] He Y J, Shen J N, Shen J F, et al. State of health detection of lithium-ion batteries: A multiscale Gaussian process regression modeling approach[J]. Aiche Journal, 2015, 61(5):1589–1600.

[11] LIAN Zhanwei, SHI Xin, KE Xiao, et al. The whole life cycle on-line detection and management system of power battery in the electric vehicle charging and exchanging station[J]. Power System Protection and Control, 2014,42(12): 137-142. [12] Zhang Chao, Xu Guoshun, Xiao Yiyang. Design and analysis of internal resistance testing equipment for high capacity lead-acid battery[J]. Chinese Journal of Power Sources, 2011, 35(9):1095-1097. [13] Guilin Zheng, Zhihao Tao, Batery internal resistance, SOC,SOH on line detection method and circuit, Patent: ZL20150309991.6